Processed proteomics dataset of undifferentiated and trilineage-differentiated KOLF2.1J cells
Description
Processed proteomics data from KOLF2.1J human induced pluripotent stem cells differentiated toward endoderm, ectoderm, and mesoderm, together with undifferentiated controls. The dataset comprises biological triplicates for each of the four states and contains the protein-level abundance table used for the proteomic analyses reported in the associated manuscript. Missing protein-abundance values were handled using a group-aware low-intensity approach, and differential protein-abundance analysis was performed using limma with Benjamini-Hochberg correction for multiple testing. The deposit also includes the analysis code and sample metadata required to reproduce the reported analyses. Raw mass-spectrometry instrument files were not supplied to the authors by the commercial service provider (BGI) and are therefore unavailable for deposition. Contains the following files: - bgi_results_total_table_annotated.xls Protein-level abundance table used as the direct input for the proteomics analysis. The table contains protein and gene annotations together with abundance measurements for 12 KOLF2.1J samples representing biological triplicates of undifferentiated, endoderm-, ectoderm-, and mesoderm-differentiated cells. - proteomics_analysis_final.R R script used to reproduce the proteomics analysis, including protein identifier filtering, group-aware low-intensity missing-value replacement, principal component analysis, limma-based differential protein-abundance analysis with Benjamini-Hochberg correction, and marker-specific visualizations. - val_genes.parquet Validated marker-gene annotation used to identify proteins belonging to the undifferentiated, endoderm, ectoderm, and mesoderm marker sets. - Readme.md Dataset documentation describing the experimental design, file contents, missing-value processing, principal component analysis, differential protein-abundance analysis, and raw-data availability.
Files
Steps to reproduce
Download all files, place them in the same folder, and run proteomics_analysis_final.R from within that folder.